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arXiv · 1911.13012

A Bayesian Approach to the Naming Game Model

Gionni Marchetti·Marco Patriarca·Els Heinsalu
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Abstract

We present a novel Bayesian approach to semiotic dynamics, which is a cognitive analogue of the naming game model restricted to two conventions. The one-shot learning that characterizes the agent dynamics in the basic naming game is replaced by a word-learning process, in which agents learn a new word by generalizing from the evidence garnered through pairwise-interactions with other agents. The principle underlying the model is that agents, like humans, can learn from a few positive examples and that such a process is modeled in a Bayesian probabilistic framework. We show that the model presents some analogies but also crucial differences with respect to the dynamics of the basic two-convention naming game model. The model introduced aims at providing a starting point for the construction of a general framework for studying the combined effects of cognitive and social dynamics.

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Gionni Marchetti, Marco Patriarca, Els Heinsalu. 2019-11-29. A Bayesian Approach to the Naming Game Model. https://doi.org/10.3389/fphy.2020.00010

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